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Comparison of Simplified SE-ResNet and SE-DenseNet for Micro-Expression Classification

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Image and Video Technology (PSIVT 2023)

Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 14403))

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Abstract

Micro-expressions are rapid and subtle facial movements that can reflect the most real emotional state hidden in the human heart. Classifying different micro-expressions is still challenging because of their short duration and low intensity. This paper proposes new neural network models, Simplified SE-DenseNet-cc and SE-ResNet-cc, incorporating Eulerian video magnification (EVM) to enlarge micro-expression movements. Important features can be selectively enhanced, and unimportant features can be compressed using SE-block. The experimental results show that our proposed methods perform better than most of the algorithms in CASME-II and SMIC.

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Authors and Affiliations

  1. Tottori University, Tottori, Japan

    Xiangbo Chen, Masashi Nishiyama & Yoshio Iwai

Authors
  1. Xiangbo Chen
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  2. Masashi Nishiyama
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  3. Yoshio Iwai
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Corresponding author

Correspondence to Yoshio Iwai .

Editor information

Editors and Affiliations

  1. Auckland University of Technology, Auckland, New Zealand

    Wei Qi Yan

  2. Auckland University of Technology, Auckland, New Zealand

    Minh Nguyen

  3. Auckland University of Technology, Auckland, New Zealand

    Parma Nand

  4. Auckland University of Technology, Auckland, New Zealand

    Xuejun Li

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Chen, X., Nishiyama, M., Iwai, Y. (2024). Comparison of Simplified SE-ResNet and SE-DenseNet for Micro-Expression Classification. In: Yan, W.Q., Nguyen, M., Nand, P., Li, X. (eds) Image and Video Technology. PSIVT 2023. Lecture Notes in Computer Science, vol 14403. Springer, Singapore. https://doi.org/10.1007/978-981-97-0376-0_26

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  • DOI: https://doi.org/10.1007/978-981-97-0376-0_26

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  • Publisher Name: Springer, Singapore

  • Print ISBN: 978-981-97-0375-3

  • Online ISBN: 978-981-97-0376-0

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